Top-down modulation impairs priming susceptibility in complex decision-making with social implications

Could social context variables prime complex decisions? Could top-down processes impair this priming susceptibility? Complex decisions have been mainly studied from economic and moral perspectives, and Dual Process Theories provide evidence of how these processes could be affected. To address these issues from a political perspective, online experiments were conducted. Participants (n = 252) were asked to choose a face from 4 options, each associated with different frequencies (repetition priming) or with phrases with different emotional valence (emotional priming), for an unspecified task (UST group) or an important task (IMT group). The most repeated face was chosen most in the UST group, and was associated with lower response times. Positive faces were equally chosen by both groups. To compare results in a more ecological situation, a social study was conducted during the 2019 Argentine Presidential Election, including online surveys (n = 3673) and analysis of news media mentioning candidates. The familiarity and trust to each candidate explained the voting-probability for most of them, as well as correlated with their frequency of mentions in the news, their positive associations, and election results. Our results suggest complex decision-making is susceptible to priming, depending on top-down modulation.

The RT model included FREQ, GROUP (and interaction FREQ:GROUP), AGE, GENDER and EDUCATION as explanatory variables. Only GROUP (see Results) and the interaction FREQ:GROUP [Chisq = 10.28, p = 0.0013] showed a significant effect. However, no significant effect was observed for FREQ itself [Chisq = 0.86,p = 0.83

Primacy and Recency
For Experimental Series #1, we analysed the data to assess whether subjects chose the faces presented in the first or last positions (Primacy or Recency effect, respectively). The Supplementary Figure 2A

Confidence analysis
Confidence was assessed using a 9-point Likert scale. An exploratory analysis showed no significant differences in mean confidence between frequency (Experiment #1) and valence (Experiment #2) or groups. However, distributional differences are usually not detectable by analysis of the mean or median; therefore, in this case we preferred to statistically assess differences in the distributions of confidence, using the cumulative probability and the Kolmogorov-Smirnov (KS) test. Significant differences were also analysed by bootstrapping.
In Experiment #1 (Experimental Series #1), different distributions of reported confidence values were observed for each group, showing a trend towards lower confidence in the IMT group. The KS test revealed a greater distance of Kolmogorov-Smirnov (D = 0.23153) between both cumulative distributions, being significant for the test (p = 0.006) and by bootstrapping (p = 8.00e-4) (Fig.1F). For Experimental Series #2, significant differences between cumulative distributions of both groups were observed only under the Lasting Condition (2000ms): the distance of KS (D = 0.1684) was only significant by bootstrapping (p = 0.02) but not significant by the KS test (p = 0.07) (Fig.2I). For Experiment #1 of Experimental Series #3, no significant differences between cumulative distributions of both groups were observed (Fig.4F), probably because these experiments had a smaller number of subjects due to the fact that they were synchronic and involved two days.
In Experiment #2, no significant differences between cumulative distributions of both groups were observed (Fig.3G). For the synchronic condition, no significant differences were detected on day 1, but they were on day 2 [KD = 0.29, p = 0.01; bootstrapping: p = 0.002) (Fig.4K).

Social Study
As described in the Results section, the Argentine Presidential Election consisted in a two-step's elections. The first step, PASO, was dated on August 11 th , 2019, and 10 candidates and their political force or coalition were presented (Suppl. Table 6). The aim of this first election is to filter the main presidential formulas to be presented in the General Election by reaching more than 1.5% of the votes. Thus, at the General Election ( [Period #2], to assess the Familiarity, Trust and Voting Probability for each candidate, as well as the social and press media the participants used to get candidate information (Supp. Table 3). For Period #1, the survey was completed by 2255 participants, of which only 2188 were included in the analysis (of which 44 were between 16 and 17 years old, the age at which voting is optional in Argentina; 1202 women). For Period #2, the survey was completed by 1418 participants, of which only 1398 were included in the analysis (of which 16 were between 16 and 17 years old, the age at which voting is optional in Argentina; 835 women). Variables were analysed by Multinomial Ordinal Model. For both periods, Voting Probability was considered the response variables, while others explanatory variables. In a first instance, rows containing NA data were removed from the original dataset (it is important to clarify that this did not necessarily imply the removal of entire subjects). From the 19700 rows of the dataset of Period #1, a total of 1283 rows were deleted (6.51%); in case of Period #2, from the 8388 rows of the dataset, a total of 674 rows were deleted (8.03%). For exploratory analysis, firstly a complete model (with all variables and no interaction) was performed in order to define which variables were significant to explain Voting Probability. Supplemental Figure 3A shows the Odds Ratios of all variables (their significance indicated by the respective asterisks) from Period #1; while Fig.  5A shows the Odds Ratios for the final model, including the significant variables. The final model was chosen as the one with lowest Akaike Information Criterion (AIC). No evidence was observed that the proportional odds assumption is not met. As described in the Results section, for Period #1, Trust and Familiarity mostly explain the variability of Voting Probability for each candidate [Odds Ratios (CI: 2.5%-9.75%): Trust: 1.95 (1.75-2.16); Familiarity: 1.31 (1.18-1.46)]. The impact of each candidate per se, or the Trust or Familiarity for each candidate per se, depend on each candidate, suggesting that main candidates (AF and MM) could have a higher effect on the final result. Another relevant variable was the Political Self-Perception, where the levels "apolitic" and "politic" had opposite effects (Fig.5A). By a Spearman correlation analysis, Voting Probability showed to correlate significantly with Trust [Spearman coeff. () = 0.82] and Familiarity [ = 0.56] (Fig.5B). During the Period #2, besides the online survey, 22,500 newspaper articles, published between September 21 and October 27 in the main written media, were collected to generate a News Dataset. Frequency mention to each candidate in the corpus or headline of the news articles published by each main written media was assessed using Text Mining tools in R. We assumed that these measures are sub-samples of the total exposure to each candidate information. In order to evaluate the positive or negative association of each candidate mention in the headlines, sentiment analysis of headlines mentioning at least one candidate was performed. For this purpose, three participants targeted the headlines as positive, negative or neutral (for each candidate), with respect to whether they perceived it as favouring the candidate's image (positive), disfavouring (negative) or simply describing a fact (neutral) (Online Methods). Participant´s agreement was evaluated by Krippendorff's alpha: all candidates showed acceptable K´alphas (>0.4), except for JGC [what could be explained by the low number of headlines that mentioned him]. The analysis of the News Dataset revealed an asymmetry in the mentions of each candidate (Fig.5C) as well as in the positive or negative perception of their headlines (Fig.5D). Our analysis allowed us to calculate the frequency of mentions and the positive, negative or neutral perception of mentions in the headlines for each candidate and for each media outlet. Taking into account that in survey each participant reported consulting different media to obtain information about the candidates, we were able to calculate the maximum frequency of exposure of each candidate (and their positive or negative perception) for each participant including these variables (EXP_CORPUS, EXP_HEAD, POS_W, NEG_W) in the original dataset. Supplemental Figure 3B shown the Odds Ratios of all variables (their significance indicated by the respective asterisks) from Period #2 (complete model); while Figure 5E shows the Odds Ratios for the final model, including only the significant variables. An interesting point to note is that the variables EXP_CORPUS and EXP_HEAD were not significant and therefore were not included in the final model. Finally, cross-correlation analysis was performed to evaluate variable means per candidate, campaign expenses and electoral results (Suppl. Table 6). This analysis allows to evidence that candidates with lower Familiarity or Trust showed also lower Voting Probability. Besides, it supports the previous results about the relationship between the Familiarity and the exposure to candidate information, even accompanied by campaign expenses [ = 0.90; p=0.012] (Fig. 5G). Besides, Trust also correlated significantly with positive mentions (in this case, expressed as the total positive mentions of each candidate: POS_T).